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Similarity measures for efficient content-based image retrieval

机译:用于基于内容的有效图像检索的相似性度量

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New similarity measures for comparing two colour histograms are described: the dissimilitude distance DS~(*) and the similarity distance E. The latter is incorporated into the exponentiation part of the Gibbs distribution and the generalised Dirichlet mixture, while the former is compared to five similarity measures: L_(1),L_(2) (Euclidean distance), the similarity measure E in addition to Gibbs and Dirichlet distributions integrating E. The proposed measures are implemented into a system called MIRA for an efficient content-based image mining and retrieval. In order to overcome the limitations (and inappropriateness) of some previous information retrieval measures in evaluating the efficiency of an image retrieval process, three variants of a new effectiveness measure are proposed and experimented on an image collection for various similarity measures, including L_(1) and L_(2). Experimental results show that retrieval effectiveness is the highest for E + Dirichlet and the lowest for the Euclidean distance. They also illustrate the superiority of our approach towards similarity analysis and retrieval effectiveness computation both in the L~(*)C~(*)H~(*) and CIECAM02 colour spaces.
机译:描述了用于比较两个颜色直方图的新的相似性度量:不相似距离DS〜(*)和相似性距离E。后者被合并到Gibbs分布的指数部分和广义Dirichlet混合中,而前者则被比较为五个相似性度量:L_(1),L_(2)(欧几里得距离),除了Gibbs和Dirichlet分布积分E之外,相似性度量E。将拟议的度量实施到称为MIRA的系统中,以实现基于内容的有效图像挖掘和恢复。为了克服某些先前的信息检索措施在评估图像检索过程的效率方面的局限性(和不适当性),提出了一种新的有效性措施的三种变体,并针对各种相似性度量对图像收集进行了实验,包括L_(1 )和L_(2)。实验结果表明,检索效率对E + Dirichlet最高,而对欧氏距离最低。它们还说明了我们在L〜(*)C〜(*)H〜(*)和CIECAM02颜色空间中进行相似性分析和检索效率计算的方法的优越性。

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